RT Journal
A1 Miraldi, Emily R.
A1 Pokrovskii, Maria
A1 Watters, Aaron
A1 Castro, Dayanne M.
A1 De Veaux, Nicholas
A1 Hall, Jason A.
A1 Lee, June-Yong
A1 Ciofani, Maria
A1 Madar, Aviv
A1 Carriero, Nick
A1 Littman, Dan R.
A1 Bonneau, Richard
T1 Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells
JF Genome Research 
JO Genome Research 
YR 2019 
FD March 01 
VO 29 
IS 3 
SP 449 
OP 463 
DO 10.1101/gr.238253.118 
UL http://genome.cshlp.org/content/29/3/449.abstract 
AB Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The assay for transposase-accessible chromatin (ATAC)–seq, coupled with TF motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to improve gene expression modeling. We test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources. In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference, combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF knockouts, and ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs (“TF–TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.